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--- |
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library_name: transformers |
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language: |
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- en |
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base_model: gokulsrinivasagan/bert_tiny_lda_50_v1_book |
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tags: |
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- generated_from_trainer |
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datasets: |
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- glue |
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metrics: |
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- accuracy |
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model-index: |
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- name: bert_tiny_lda_50_v1_book_mnli |
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results: |
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- task: |
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name: Text Classification |
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type: text-classification |
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dataset: |
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name: GLUE MNLI |
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type: glue |
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args: mnli |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.7529495524816925 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# bert_tiny_lda_50_v1_book_mnli |
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This model is a fine-tuned version of [gokulsrinivasagan/bert_tiny_lda_50_v1_book](https://huggingface.co/gokulsrinivasagan/bert_tiny_lda_50_v1_book) on the GLUE MNLI dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6057 |
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- Accuracy: 0.7529 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 256 |
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- eval_batch_size: 256 |
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- seed: 10 |
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- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- num_epochs: 50 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:| |
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| 0.7973 | 1.0 | 1534 | 0.7054 | 0.6963 | |
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| 0.6499 | 2.0 | 3068 | 0.6406 | 0.7318 | |
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| 0.5649 | 3.0 | 4602 | 0.6320 | 0.7460 | |
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| 0.4962 | 4.0 | 6136 | 0.6454 | 0.7494 | |
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| 0.4374 | 5.0 | 7670 | 0.6634 | 0.7514 | |
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| 0.3831 | 6.0 | 9204 | 0.7042 | 0.7517 | |
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| 0.3325 | 7.0 | 10738 | 0.7310 | 0.7552 | |
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| 0.2885 | 8.0 | 12272 | 0.8192 | 0.7524 | |
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### Framework versions |
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- Transformers 4.46.3 |
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- Pytorch 2.2.1+cu118 |
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- Datasets 2.17.0 |
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- Tokenizers 0.20.3 |
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